Data augmentations.
Data augmentation is a widely used technique to improve the generalization.
Besides classical image transformations like random flipping or cropping, many other approaches have been proposed such as linearly interpolating between images and their labels [1]}, replacing a part of the image with either a black-colored patch [2]} or a part of another image [3]}.
One of the best-performing methods in terms of accuracy and calibration on common corruptions is AugMix [4]}, which combines carefully selected augmentations with a regularization term based on the Jensen-Shannon divergence.
[5]} observe that improvements on synthetic distribution shifts (such as common corruptions) do not necessarily transfer to real distribution shifts.
However, [6]} show an example when improving robustness against synthetic blurs also helps against naturally obtained blurred images.
